The recent trends show that “Internet of Things (IoT)” is becoming a powerful business transformation force, and its disruptive impact is being felt across all industries and all areas of society. The internet of things (IoT) is the network of physical devices, vehicles, buildings and other items—embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data. Internet of Things has the potential to bring in major changes in business models with potentially one trillion connected devices across the world. The unprecedented level of connectivity mandates new ideas and innovations encompassing several domains such as e-Governance, Health Care, Transportation, and Utilities etc. and also delves into the development of IoT analytics applications.
IoT analytics application development requires domain knowledge, sensor data analytics expertise, coding expertise, and knowledge about infrastructures so that it can be deployed properly. This is traditionally known as the four stake holders of IoT application development. But it is not possible for a single person to have all these knowledge. So the cost of application development increases in two aspects (i) time to development increases and (ii) the cost of hiring resources with niche skill set is also very high which in turn results into increase in the cost of the product. As a consequence there is a need of automation in IoT application development so that the time to market and the cost of hiring employees with niche skill set can be reduced. It is mandatory to involve people with domain knowledge as they mainly provides the problem but the effort from a signal processing expert and coder can be reduced if one can capture their knowledge and use it properly.
Some efforts have been made in the past for the automation of IoT application development. These methods are trying to capture the knowledge of a sensor signal processing expert and also get the corresponding codes from open sources. But two major things are missing (i) what are the steps for a sensor signal processing/analytics algorithm and (ii) which steps are required to be automated to reduce sensor signal processing expert's involvement. It has been found out that the most time consuming step in IoT application development is the feature selection. Deep learning is now a days very common for feature extraction in the domain of image processing and natural language processing (NLP). But in case of IoT application development there is a requirement to interpret the recommended features that Deep learning method can't serve.